INTRODUCTION
Sierra Object using GSE123904 (Lung Cancer Data using Normal, Tumor and Metastasis Data) prepared using Sierra, Scran and Scater packages. DUTest function is used for differential alternative splicing analysis (via DEXSeq package).
Required Package Import
set.seed(9999)
gtf_gr <- rtracklayer::import("GRCh38.108_ensembl.chr_filtered.gtf")
Pre-Processed Data Import
load("rta_data.RData")
Cell Type Abbreviations
DENDRITIC=Dendritic Cells ENDOTHELIAL=Endothelial Cells EPITHELIAL=Epithelial (and Tumor) Cells FIBROBLAST=Fibroblasts IG=Ig MACROPHAGE=Macrophages MAST=Mast Cells MDSC=Myeloid Derived Suppressor Cells MICROGLIA=Microglia MONOCYTE=Monocyte NEUTROPHIL=Neutrophil NK=Natural Killer Cells NKT=Natural Killer Like T Cells PERICYTE=Pericytes PROLIFERATING=Proliferating Cells Th=T Helper Cells Tm=Memory T Cells Treg=Regulatory T Cells
UMAP and TSNE Plots of Cell Types, Data Source and Cell Labels
plotReducedDim(merged_sce , dimred = "TSNE" , colour_by = "cell_type")

plotReducedDim(merged_sce , dimred = "TSNE" , colour_by = "source")

plotReducedDim(merged_sce , dimred = "TSNE" , colour_by = "cell_label")

Number of Cells in Each Cell Type,Data Source and Cell Label
ggplot(data=as.data.frame(table(merged_sce$cell_type)), aes(x=Var1, y=Freq, fill = Var1, label = Freq)) + geom_bar(stat="identity")+theme_minimal() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + geom_text(size = 5, position = position_stack(vjust = 0.5)) + theme(legend.position = "none")

ggplot(data=as.data.frame(table(merged_sce$source)), aes(x=Var1, y=Freq, fill = Var1, label = Freq)) + geom_bar(stat="identity")+theme_minimal() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + geom_text(size = 10, position = position_stack(vjust = 0.5))+ theme(legend.position = "none")

ggplot(data=as.data.frame(table(merged_sce$cell_label)), aes(x=Var1, y=Freq, fill = Var1, label = Freq)) + geom_bar(stat="identity")+theme_minimal() + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + geom_text(size = 3, position = position_stack(vjust = 0.5))+ theme(legend.position = "none")

Cell Labels with more than 500 cells in it
Due to low number of cells in some cell label clusters, statistical analysis will be biased.
kable(as.data.frame(table(merged_sce$cell_label)[table(merged_sce$cell_label) > 500]) , digits = 4, "simple")
| Breg_Tumor |
2458 |
| DENDRITIC_Tumor |
697 |
| ENDOTHELIAL_Tumor |
776 |
| EPITHELIAL_Met |
981 |
| EPITHELIAL_Tumor |
1514 |
| MACROPHAGE_Normal |
2394 |
| MACROPHAGE_Tumor |
981 |
| MAST_Tumor |
654 |
| MDSC_Tumor |
701 |
| MONOCYTE_Normal |
727 |
| MONOCYTE_Tumor |
656 |
| NK_Normal |
1769 |
| NKT_Met |
559 |
| NKT_Normal |
1386 |
| NKT_Tumor |
1078 |
| PERICYTE_Met |
702 |
| Th_Met |
1249 |
| Th_Normal |
1024 |
| Th_Tumor |
7014 |
| Tm_Met |
2443 |
| Tm_Normal |
2169 |
| Tm_Tumor |
5103 |
| Treg_Tumor |
838 |
| # Tumor and Metastat |
is Sample Comparisons (T Cells) |
Comparison of Memory T Cells of Tumor and Metastatsis Samples
tm_met_tumor <- DUTest(peaks_sce,
population.1 = "Tm_Met",
population.2 = "Tm_Tumor",
exp.thresh = 0.1,
feature.type = c("UTR3", "exon" , "intron"))
## [1] "4263 expressed peaks in feature types UTR3, exon, intron"
## [1] "1042 genes detected with multiple peak sites expressed"
## [1] "3001 individual peak sites to test"
## [1] "Running DEXSeq test..."
tm_met_tumor.top <- subset(tm_met_tumor, abs(Log2_fold_change) > 1)
Results Table (Memory T Cells)
kable(tm_met_tumor.top , digits = 4, "simple")
| RPS27A:2:55232649-55232899:1 |
RPS27A |
UTR5;intron;exon |
0.6246 |
0.6083 |
0 |
0 |
-1.0279 |
| COQ10B:2:197472359-197472832:1 |
COQ10B |
intron |
0.1347 |
0.0145 |
0 |
0 |
2.5804 |
| COQ10B:2:197474702-197475278:1 |
COQ10B |
UTR3 |
0.0995 |
0.1170 |
0 |
0 |
-1.8355 |
| CYBA:16:88648868-88649464:-1 |
CYBA |
intron |
0.0995 |
0.1875 |
0 |
0 |
-1.6545 |
| CYBA:16:88648920-88649322:-1 |
CYBA |
intron |
0.0966 |
0.1820 |
0 |
0 |
-1.6459 |
| HSP90AA1:14:102081942-102082536:-1 |
HSP90AA1 |
intron;exon |
0.1772 |
0.0500 |
0 |
0 |
1.6041 |
| HSPD1:2:197512969-197513353:-1 |
HSPD1 |
intron |
0.1273 |
0.0090 |
0 |
0 |
3.3871 |
| SIK3:11:117035701-117036150:-1 |
SIK3 |
intron |
0.2374 |
0.1715 |
0 |
0 |
1.3249 |
| KRR1:12:75495784-75496863:-1 |
KRR1 |
UTR3 |
0.0393 |
0.1282 |
0 |
0 |
-1.4371 |
| KRR1:12:75499261-75506890:-1 |
KRR1 |
UTR3 |
0.1199 |
0.0790 |
0 |
0 |
1.2529 |
| HMGB1:13:30614865-30615327:-1 |
HMGB1 |
intron |
0.1371 |
0.0268 |
0 |
0 |
2.5002 |
| CTLA4:2:203873528-203873965:1 |
CTLA4 |
UTR3 |
0.1347 |
0.0586 |
0 |
0 |
1.2629 |
| CTLA4:2:203868202-203868704:1 |
CTLA4 |
intron |
0.0905 |
0.1225 |
0 |
0 |
-1.2534 |
| PABPC1:8:100719368-100719845:-1 |
PABPC1 |
intron;exon |
0.0896 |
0.1879 |
0 |
0 |
-1.0481 |
| PTPRC:1:198680020-198680476:1 |
PTPRC |
intron |
0.0532 |
0.1256 |
0 |
0 |
-1.2871 |
| HSPH1:13:31159811-31161149:-1 |
HSPH1 |
intron |
0.1015 |
0.0259 |
0 |
0 |
1.7874 |
| RGPD2:2:87805535-87805933:-1 |
RGPD2 |
intron |
0.0282 |
0.1117 |
0 |
0 |
-1.2834 |
| PTPRC:1:198665713-198666361:1 |
PTPRC |
intron |
0.0864 |
0.1640 |
0 |
0 |
-1.0026 |
| ARHGAP15:2:143155606-143156068:1 |
ARHGAP15 |
intron;exon |
0.1044 |
0.0390 |
0 |
0 |
1.0383 |
de_genes_as_tm <- tm_met_tumor.top$gene_name
go_res_as_tm <- enrichGO(de_genes_as_tm, OrgDb = "org.Hs.eg.db", keyType = "SYMBOL" , ont = "BP")
dotplot(go_res_as_tm , showCategory = 15)

Plots of Differentially Alternative Spliced Regions (Memory T Cells)
tm_coq10b.plot <- rownames(subset(tm_met_tumor.top, gene_name == "COQ10B"))
tm_krr1.plot <- rownames(subset(tm_met_tumor.top, gene_name == "KRR1"))
tm_ctla4.plot <- rownames(subset(tm_met_tumor.top, gene_name == "CTLA4"))
PlotRelativeExpressionTSNE(peaks_sce, peaks.to.plot = tm_coq10b.plot)

PlotRelativeExpressionTSNE(peaks_sce, peaks.to.plot = tm_krr1.plot)

PlotRelativeExpressionTSNE(peaks_sce, peaks.to.plot = tm_ctla4.plot)

PlotRelativeExpressionBox(peaks_sce, peaks.to.plot = tm_coq10b.plot)

PlotRelativeExpressionBox(peaks_sce, peaks.to.plot = tm_krr1.plot)

PlotRelativeExpressionBox(peaks_sce, peaks.to.plot = tm_ctla4.plot)

Coverage Plots (Memory T Cells)
outdir = "bam_subsets/"
bam.files_rps27A_tm <- paste0(outdir, c("Memory_T_Cells_Tumor.RPS27A.bam", "Memory_T_Cells_Metastasis.RPS27A.bam"))
bam.files_coq10b_tm <- paste0(outdir, c("Memory_T_Cells_Tumor.COQ10B.bam", "Memory_T_Cells_Metastasis.COQ10B.bam"))
bam.files_krr1_tm <- paste0(outdir, c("Memory_T_Cells_Tumor.KRR1.bam", "Memory_T_Cells_Metastasis.KRR1.bam"))
bam.files_ctla4_tm <- paste0(outdir, c("Memory_T_Cells_Tumor.CTLA4.bam", "Memory_T_Cells_Metastasis.CTLA4.bam"))
PlotCoverage(genome_gr = gtf_gr,
geneSymbol = "RPS27A",
genome = "hg38", label.transcripts = TRUE,
bamfiles = bam.files_rps27A_tm,
bamfile.tracknames=c("Tumor", "Metastasis"),
peaks.annot = rownames(subset(tm_met_tumor.top, gene_name == "RPS27A")))

PlotCoverage(genome_gr = gtf_gr,
geneSymbol = "COQ10B",
genome = "hg38", label.transcripts = TRUE,
bamfiles = bam.files_coq10b_tm,
bamfile.tracknames=c("Tumor", "Metastasis"),
peaks.annot = rownames(subset(tm_met_tumor.top, gene_name == "COQ10B")))

PlotCoverage(genome_gr = gtf_gr,
geneSymbol = "KRR1",
genome = "hg38", label.transcripts = TRUE,
bamfiles = bam.files_krr1_tm,
bamfile.tracknames=c("Tumor", "Metastasis"),
peaks.annot = rownames(subset(tm_met_tumor.top, gene_name == "KRR1")))

PlotCoverage(genome_gr = gtf_gr,
geneSymbol = "CTLA4",
genome = "hg38", label.transcripts = TRUE,
bamfiles = bam.files_ctla4_tm,
bamfile.tracknames=c("Tumor", "Metastasis"),
peaks.annot = rownames(subset(tm_met_tumor.top, gene_name == "CTLA4")))

Heatmap of Selected Regions with AS Events and Genes With Selected Regions (Memory T Cells)
peak_counts_matrix <- counts(peaks_sce)
colnames(peak_counts_matrix) <- as.character(peaks_sce$CellIdent)
counts_of_selected_regions <- as.matrix(peak_counts_matrix[as.character(c(tm_coq10b.plot,tm_krr1.plot,tm_ctla4.plot)),])
counts_of_selected_regions <- counts_of_selected_regions[,colnames(peak_counts_matrix) %like% 'Tm_Met|Tm_Tumor']
counts_of_selected_regions <- cbind(rowSums(counts_of_selected_regions[,colnames(counts_of_selected_regions) == "Tm_Tumor"]),rowSums(counts_of_selected_regions[,colnames(counts_of_selected_regions) == "Tm_Met"]))
colnames(counts_of_selected_regions) <- c("Memory T Cells (Tumor)" , "Memory T Cells (Metastasis)")
counts_of_selected_regions <- as.data.frame(melt(counts_of_selected_regions))
colnames(counts_of_selected_regions) <- c("Region" , "Cell_Label" , "Counts")
Heatmap of Counts of Genes With Selected Regions (Memory T Cells)
counts_matrix <- counts(merged_sce)
colnames(counts_matrix) <- as.character(peaks_sce$CellIdent)
counts_of_selected_genes <- as.matrix(counts_matrix[as.character(c("COQ10B","KRR1","CTLA4")),])
counts_of_selected_genes <- counts_of_selected_genes[,colnames(counts_matrix) %like% 'Tm_Met|Tm_Tumor']
counts_of_selected_genes <- cbind(rowSums(counts_of_selected_genes[,colnames(counts_of_selected_genes) == "Tm_Tumor"]),rowSums(counts_of_selected_genes[,colnames(counts_of_selected_genes) == "Tm_Met"]))
colnames(counts_of_selected_genes) <- c("Memory T Cells (Tumor)" , "Memory T Cells (Metastasis)")
counts_of_selected_genes <- as.data.frame(melt(counts_of_selected_genes))
colnames(counts_of_selected_genes) <- c("Gene" , "Cell_Label" , "Counts")
p1 <- ggplot(counts_of_selected_genes, aes(Cell_Label, Gene, fill= Counts , label = Counts)) + geom_tile(color = "black") + scale_fill_gradient(low = "white", high = "red") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + geom_text(size = 2)+ theme(legend.position = "none")
p2 <- ggplot(counts_of_selected_regions, aes(Cell_Label, Region, fill= Counts , label = Counts)) + geom_tile(color = "black") + scale_fill_gradient(low = "white", high = "red") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + geom_text(size = 2)+ theme(legend.position = "none")
grid.arrange(p1, p2, ncol = 2)

Comparison of Helper T Cells of Tumor and Metastatsis Samples
th_met_tumor <- DUTest(peaks_sce,
population.1 = "Th_Met",
population.2 = "Th_Tumor",
exp.thresh = 0.1,
feature.type = c("UTR3", "exon" , "intron"))
## [1] "5181 expressed peaks in feature types UTR3, exon, intron"
## [1] "1218 genes detected with multiple peak sites expressed"
## [1] "3825 individual peak sites to test"
## [1] "Running DEXSeq test..."
th_met_tumor.top <- subset(th_met_tumor, abs(Log2_fold_change) > 1)
Results Table (T Helper Cells)
kable(th_met_tumor.top, digits = 4)
| RPS27A:2:55232649-55232899:1 |
RPS27A |
UTR5;intron;exon |
0.3875 |
0.6139 |
0 |
0 |
-2.1913 |
| HSPA1B:6:31828867-31829767:1 |
HSPA1B |
exon |
0.1265 |
0.4224 |
0 |
0 |
-2.0355 |
| TNFAIP3:6:137868239-137868765:1 |
TNFAIP3 |
intron |
0.3923 |
0.4672 |
0 |
0 |
1.1412 |
| HSPA1B:6:31829657-31829729:1 |
HSPA1B |
exon |
0.0849 |
0.3474 |
0 |
0 |
-2.2398 |
| HSPA1B:6:31829660-31829709:1 |
HSPA1B |
exon |
0.0745 |
0.3251 |
0 |
0 |
-2.3069 |
| CTLA4:2:203873528-203873965:1 |
CTLA4 |
UTR3 |
0.2738 |
0.0853 |
0 |
0 |
1.0296 |
| CTLA4:2:203868202-203868704:1 |
CTLA4 |
intron |
0.1089 |
0.1347 |
0 |
0 |
-1.6298 |
| SERF2:15:43792311-43792377:1 |
SERF2 |
UTR5;exon |
0.1649 |
0.3332 |
0 |
0 |
-1.2178 |
| SIK3:11:117035701-117036150:-1 |
SIK3 |
intron |
0.2706 |
0.2066 |
0 |
0 |
1.2743 |
| COQ10B:2:197472359-197472832:1 |
COQ10B |
intron |
0.1153 |
0.0442 |
0 |
0 |
1.6068 |
| SERF2:15:43792323-43792335:1 |
SERF2 |
UTR5;exon |
0.0929 |
0.2275 |
0 |
0 |
-1.3992 |
| HSPD1:2:197512969-197513353:-1 |
HSPD1 |
intron |
0.1577 |
0.0488 |
0 |
0 |
2.0118 |
| HLA-DQA1:6:32642964-32643102:1 |
HLA-DQA1 |
UTR3 |
0.2090 |
0.0632 |
0 |
0 |
1.2836 |
| RSRC2:12:122525695-122526454:-1 |
RSRC2 |
UTR5;intron;exon |
0.1209 |
0.0315 |
0 |
0 |
2.0915 |
| ANXA1:9:73167471-73169062:1 |
ANXA1 |
intron;exon |
0.0785 |
0.2298 |
0 |
0 |
-1.0252 |
| HMGB1:13:30614865-30615327:-1 |
HMGB1 |
intron |
0.1393 |
0.0647 |
0 |
0 |
1.6904 |
| AMBRA1:11:46429015-46429153:-1 |
AMBRA1 |
intron |
0.0256 |
0.1014 |
0 |
0 |
-2.1893 |
| LDLRAD4:18:13645330-13645842:1 |
LDLRAD4 |
UTR3 |
0.0384 |
0.1473 |
0 |
0 |
-1.4014 |
| HSPA8:11:123057857-123058532:-1 |
HSPA8 |
intron;exon |
0.0945 |
0.3044 |
0 |
0 |
-1.1599 |
| CYBA:16:88648920-88649322:-1 |
CYBA |
intron |
0.1025 |
0.1583 |
0 |
0 |
-1.2269 |
| CYBA:16:88648868-88649464:-1 |
CYBA |
intron |
0.1073 |
0.1660 |
0 |
0 |
-1.2370 |
| RPL6:12:112406330-112406921:-1 |
RPL6 |
intron;exon |
0.1217 |
0.2448 |
0 |
0 |
-1.0156 |
| ELOVL5:6:53294679-53295146:-1 |
ELOVL5 |
intron |
0.0256 |
0.1153 |
0 |
0 |
-1.7868 |
| SCML4:6:107704636-107705095:-1 |
SCML4 |
UTR3 |
0.0608 |
0.1056 |
0 |
0 |
-1.0972 |
| HSP90AA1:14:102085986-102086154:-1 |
HSP90AA1 |
UTR3 |
0.0440 |
0.1587 |
0 |
0 |
-1.4266 |
| RPS13:11:17077411-17077709:-1 |
RPS13 |
UTR5;intron;exon |
0.0424 |
0.1212 |
0 |
0 |
-1.4533 |
| SARAF:8:30063445-30063799:-1 |
SARAF |
UTR3 |
0.0232 |
0.1236 |
0 |
0 |
-1.6024 |
| SPOCK2:10:72086250-72086830:-1 |
SPOCK2 |
UTR3 |
0.0336 |
0.1035 |
0 |
0 |
-1.2780 |
| SQSTM1:5:179836655-179836853:1 |
SQSTM1 |
UTR3 |
0.0520 |
0.1635 |
0 |
0 |
-1.1312 |
| KTN1:14:55580202-55580250:1 |
KTN1 |
UTR5;intron;exon |
0.0368 |
0.1319 |
0 |
0 |
-1.1552 |
| BCL11B:14:99272050-99272197:-1 |
BCL11B |
UTR5;exon |
0.0320 |
0.1156 |
0 |
0 |
-1.1209 |
de_genes_as_th <- th_met_tumor.top$gene_name
go_res_as_th <- enrichGO(de_genes_as_th, OrgDb = "org.Hs.eg.db", keyType = "SYMBOL" , ont = "BP")
barplot(go_res_as_th , showCategory = 20)

Plots of Differentially Alternative Spliced Regions (T Helper Cells)
th_cyba.plot <- rownames(subset(th_met_tumor.top, gene_name == "CYBA"))
th_ctla4.plot <- rownames(subset(th_met_tumor.top, gene_name == "CTLA4"))
th_hspa1b.plot <- rownames(subset(th_met_tumor.top, gene_name == "HSPA1B"))
PlotRelativeExpressionTSNE(peaks_sce, peaks.to.plot = th_cyba.plot)

PlotRelativeExpressionTSNE(peaks_sce, peaks.to.plot = th_ctla4.plot)

PlotRelativeExpressionTSNE(peaks_sce, peaks.to.plot = th_hspa1b.plot)

PlotRelativeExpressionBox(peaks_sce, peaks.to.plot = th_cyba.plot)

PlotRelativeExpressionBox(peaks_sce, peaks.to.plot = th_ctla4.plot)

PlotRelativeExpressionBox(peaks_sce, peaks.to.plot = th_hspa1b.plot)

Coverage Plots (Helper T Cells)
outdir = "bam_subsets/"
bam.files_hspa1b_th <- paste0(outdir, c("Helper_T_Cells_Tumor.HSPA1B.bam", "Helper_T_Cells_Metastasis.HSPA1B.bam"))
bam.files_rps27a_th <- paste0(outdir, c("Helper_T_Cells_Tumor.RPS27A.bam", "Helper_T_Cells_Metastasis.RPS27A.bam"))
bam.files_ctla4_th <- paste0(outdir, c("Helper_T_Cells_Tumor.CTLA4.bam", "Helper_T_Cells_Metastasis.CTLA4.bam"))
bam.files_cyba_th <- paste0(outdir, c("Helper_T_Cells_Tumor.CYBA.bam", "Helper_T_Cells_Metastasis.CYBA.bam"))
bam.files_ambra_th <- paste0(outdir, c("Helper_T_Cells_Tumor.AMBRA1.bam", "Helper_T_Cells_Metastasis.AMBRA1.bam"))
PlotCoverage(genome_gr = gtf_gr,
geneSymbol = "HSPA1B",
genome = "hg38", label.transcripts = TRUE,
bamfiles = bam.files_hspa1b_th,
bamfile.tracknames=c("Tumor", "Metastasis"),
peaks.annot = rownames(subset(th_met_tumor.top, gene_name == "HSPA1B")))

PlotCoverage(genome_gr = gtf_gr,
geneSymbol = "RPS27A",
genome = "hg38", label.transcripts = TRUE,
bamfiles = bam.files_rps27a_th,
bamfile.tracknames=c("Tumor", "Metastasis"),
peaks.annot = rownames(subset(th_met_tumor.top, gene_name == "RPS27A")))

PlotCoverage(genome_gr = gtf_gr,
geneSymbol = "CTLA4",
genome = "hg38", label.transcripts = TRUE,
bamfiles = bam.files_ctla4_th,
bamfile.tracknames=c("Tumor", "Metastasis") , peaks.annot = th_ctla4.plot)

PlotCoverage(genome_gr = gtf_gr,
geneSymbol = "CYBA",
genome = "hg38", label.transcripts = TRUE,
bamfiles = bam.files_cyba_th,
bamfile.tracknames=c("Tumor", "Metastasis") , peaks.annot = th_cyba.plot)

PlotCoverage(genome_gr = gtf_gr,
geneSymbol = "AMBRA1",
genome = "hg38", label.transcripts = TRUE,
bamfiles = bam.files_ambra_th,
bamfile.tracknames=c("Tumor", "Metastasis") ,
peaks.annot = rownames(subset(th_met_tumor.top, gene_name == "AMBRA1")))

Heatmap of Selected Regions with AS Events and Genes With Selected Regions (T Helper Cells)
peak_counts_matrix <- counts(peaks_sce)
colnames(peak_counts_matrix) <- as.character(peaks_sce$CellIdent)
counts_of_selected_regions <- as.matrix(peak_counts_matrix[as.character(c(th_cyba.plot,th_ctla4.plot)),])
counts_of_selected_regions <- counts_of_selected_regions[,colnames(peak_counts_matrix) %like% 'Th_Met|Th_Tumor']
counts_of_selected_regions <- cbind(rowSums(counts_of_selected_regions[,colnames(counts_of_selected_regions) == "Th_Tumor"]),rowSums(counts_of_selected_regions[,colnames(counts_of_selected_regions) == "Th_Met"]))
colnames(counts_of_selected_regions) <- c("Helper T Cells (Tumor)" , "Helper T Cells (Metastasis)")
counts_of_selected_regions <- as.data.frame(melt(counts_of_selected_regions))
colnames(counts_of_selected_regions) <- c("Region" , "Cell_Label" , "Counts")
Heatmap of Counts of Genes With Selected Regions (T Helper Cells)
counts_matrix <- counts(merged_sce)
colnames(counts_matrix) <- as.character(peaks_sce$CellIdent)
counts_of_selected_genes <- as.matrix(counts_matrix[as.character(c("CYBA","CTLA4")),])
counts_of_selected_genes <- counts_of_selected_genes[,colnames(counts_matrix) %like% 'Th_Met|Th_Tumor']
counts_of_selected_genes <- cbind(rowSums(counts_of_selected_genes[,colnames(counts_of_selected_genes) == "Th_Tumor"]),rowSums(counts_of_selected_genes[,colnames(counts_of_selected_genes) == "Th_Met"]))
colnames(counts_of_selected_genes) <- c("Helper T Cells (Tumor)" , "Helper T Cells (Metastasis)")
counts_of_selected_genes <- as.data.frame(melt(counts_of_selected_genes))
colnames(counts_of_selected_genes) <- c("Gene" , "Cell_Label" , "Counts")
p1 <- ggplot(counts_of_selected_genes, aes(Cell_Label, Gene, fill= Counts , label = Counts)) + geom_tile(color = "black") + scale_fill_gradient(low = "white", high = "red") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + geom_text(size = 2)+ theme(legend.position = "none")
p2 <- ggplot(counts_of_selected_regions, aes(Cell_Label, Region, fill= Counts , label = Counts)) + geom_tile(color = "black") + scale_fill_gradient(low = "white", high = "red") + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + geom_text(size = 2)+ theme(legend.position = "none")
grid.arrange(p1, p2, ncol = 2)

SUBCLUSTER AND TRAJECTORY ANALYSIS (Memory T Cells)
peak_counts_matrix <- counts(peaks_sce)
colnames(peak_counts_matrix) <- as.character(peaks_sce$CellIdent)
tm_sce_peak_counts <- counts(peaks_sce)[,colnames(peak_counts_matrix) %like% 'Tm_Met|Tm_Tumor']
tm_sce_peak_counts <- tm_sce_peak_counts[rowSums(tm_sce_peak_counts) > 7,]
tm_sce_peak <- SingleCellExperiment(assays = list(counts = tm_sce_peak_counts))
tm_sce_peak$cell_labels <- as.character(peaks_sce$CellIdent)[colnames(peak_counts_matrix) %like% 'Tm_Met|Tm_Tumor']
clusters <- quickCluster(tm_sce_peak , method = "igraph")
tm_sce_peak <- computeSumFactors(tm_sce_peak , cluster=clusters)
tm_sce_peak$clusters <- clusters
tm_sce_peak <- logNormCounts(tm_sce_peak)
dec <- modelGeneVarByPoisson(tm_sce_peak)
top <- getTopHVGs(dec , n = 4000)
tm_sce_peak <- denoisePCA(tm_sce_peak, subset.row=top, technical=dec)
tm_sce_peak <- runTSNE(tm_sce_peak)
tm_sce_peak <- runUMAP(tm_sce_peak)
plotPCA(tm_sce_peak , colour_by = "cell_labels")

plotUMAP(tm_sce_peak , colour_by = "cell_labels")

plotTSNE(tm_sce_peak , colour_by = "cell_labels")

library(slingshot)
library(RColorBrewer)
tm_sce_peak <- slingshot(tm_sce_peak, reducedDim='PCA' , clusterLabels = as.character(tm_sce_peak$cell_labels) , start.clus = "Tm_Tumor" , end.clus = "Tm_Met")
embedded <- embedCurves(tm_sce_peak, "UMAP")
embedded <- slingCurves(embedded)[[1]]
embedded <- data.frame(embedded$s[embedded$ord,])
plotUMAP(tm_sce_peak, colour_by="slingPseudotime_1") +
geom_path(data=embedded, aes(x=Dim.1, y=Dim.2), linewidth=1)

library(tradeSeq)
# fit negative binomial GAM
sce <- fitGAM(counts = as.matrix(counts(tm_sce_peak)[top,]), sds = as.SlingshotDataSet(tm_sce_peak))
colnames(sce) <- tm_sce_peak$cell_labels
# test for dynamic expression
ATres_tm <- associationTest(sce)
ATres_tm <- ATres_tm[!(is.na(ATres_tm$pvalue)),]
ATres_tm$padj <- p.adjust(ATres_tm$pvalue)
ATres_tm <- ATres_tm[ATres_tm$meanLogFC > 1,]
topgenes <- rownames(ATres_tm[order(ATres_tm$padj), ])[1:50]
pst.ord <- order(tm_sce_peak$slingPseudotime_1, na.last = NA)
heatdata <- assays(tm_sce_peak)$counts[topgenes, pst.ord]
heatclus <- tm_sce_peak$cell_labels[pst.ord]
colnames(heatdata) <- tm_sce_peak$cell_labels
heatdata <- heatdata[ , order(colnames(heatdata))]
heatmap(log1p(as.matrix(heatdata)), Colv = NA, Rowv = NA, ColSideColors = brewer.pal(9,"Set1")[heatclus])

kable(ATres_tm[1:50,], digits = 4, "simple" , n = 100)
| MT-CO1:MT:7034-7445:1 |
4564.5721 |
5 |
0 |
2.1038 |
0 |
| HSPA1A:6:31817504-31817946:1 |
764.4294 |
5 |
0 |
3.0246 |
0 |
| MT-CO1:MT:6504-7267:1 |
4118.2110 |
5 |
0 |
2.0657 |
0 |
| JUNB:19:12792890-12793315:1 |
3608.8050 |
5 |
0 |
1.2618 |
0 |
| HSP90AA1:14:102084861-102086016:-1 |
702.9817 |
5 |
0 |
3.0941 |
0 |
| RPS26:12:56042140-56044274:1 |
1241.6470 |
5 |
0 |
5.4014 |
0 |
| FTH1:11:61964510-61965102:-1 |
3398.9904 |
5 |
0 |
1.9014 |
0 |
| MT-CO1:MT:6652-7208:1 |
3540.5284 |
5 |
0 |
1.9577 |
0 |
| MT-CO3:MT:9540-9990:1 |
1455.3090 |
5 |
0 |
2.4399 |
0 |
| RGS1:1:192579470-192580024:1 |
2323.4696 |
5 |
0 |
3.6202 |
0 |
| ACTB:7:5527137-5527581:-1 |
1081.7246 |
5 |
0 |
5.4080 |
0 |
| MT-CO2:MT:7859-8269:1 |
1783.8240 |
5 |
0 |
2.2161 |
0 |
| MT-ND2:MT:5086-5511:1 |
1355.7948 |
5 |
0 |
2.4167 |
0 |
| CREM:10:35206785-35212140:1 |
3494.5127 |
5 |
0 |
2.0962 |
0 |
| KLRB1:12:9595207-9599795:-1 |
104.7249 |
5 |
0 |
4.8859 |
0 |
| CCL5:17:35871491-35871840:-1 |
777.7726 |
5 |
0 |
5.4304 |
0 |
| MT-CYB:MT:15450-15887:1 |
1230.2569 |
5 |
0 |
2.3603 |
0 |
| MT-ND3:MT:10059-10404:1 |
1596.9076 |
5 |
0 |
2.1687 |
0 |
| BTG1:12:92143918-92144376:-1 |
4292.1723 |
5 |
0 |
2.2845 |
0 |
| MT-ATP6:MT:8805-9207:1 |
1446.6504 |
5 |
0 |
2.1098 |
0 |
| LTB:6:31580525-31580955:-1 |
537.3039 |
5 |
0 |
5.7379 |
0 |
| S100A4:1:153543613-153544685:-1 |
432.6943 |
5 |
0 |
6.4740 |
0 |
| S100A4:1:153543613-153543921:-1 |
439.8091 |
5 |
0 |
6.4695 |
0 |
| MT-ND4:MT:11699-12137:1 |
1720.9808 |
5 |
0 |
2.2563 |
0 |
| SRGN:10:69104309-69104805:1 |
3460.9507 |
5 |
0 |
1.2236 |
0 |
| MT-ND1:MT:3804-4262:1 |
1449.1800 |
5 |
0 |
2.7541 |
0 |
| MT-ND4:MT:11137-11875:1 |
1576.1172 |
5 |
0 |
2.1683 |
0 |
| ZFP36:19:39408952-39409412:1 |
3429.5910 |
5 |
0 |
2.1000 |
0 |
| CCL4:17:36104582-36105621:1 |
533.9682 |
5 |
0 |
5.7116 |
0 |
| CCL4:17:36105200-36105621:1 |
534.0629 |
5 |
0 |
5.7297 |
0 |
| ZFP36L2:2:43224000-43224428:-1 |
2622.9707 |
5 |
0 |
1.1621 |
0 |
| RPL3:22:39312882-39313702:-1 |
8488.9454 |
5 |
0 |
2.9296 |
0 |
| RPL3:22:39312882-39313309:-1 |
5859.5678 |
5 |
0 |
2.9356 |
0 |
| IL32:16:3069102-3069591:1 |
759.4712 |
5 |
0 |
6.0178 |
0 |
| RPL21:13:27253840-27256629:1 |
6131.3842 |
5 |
0 |
3.7306 |
0 |
| RPS12:6:132814970-132817564:1 |
6279.4670 |
5 |
0 |
3.7757 |
0 |
| EEF1A1:6:73517465-73518061:-1 |
5637.5941 |
5 |
0 |
3.0648 |
0 |
| RPL21:13:27255251-27256597:1 |
4521.8221 |
5 |
0 |
3.7546 |
0 |
| TSC22D3:X:107713221-107713546:-1 |
1598.6706 |
5 |
0 |
1.7981 |
0 |
| RPS6:9:19376184-19378727:-1 |
5755.8542 |
5 |
0 |
3.4574 |
0 |
| RPS18:6:33272694-33276511:1 |
6911.1627 |
5 |
0 |
4.1537 |
0 |
| RPS18:6:33275780-33276511:1 |
10814.0514 |
5 |
0 |
4.1404 |
0 |
| BTG1:12:92143068-92143573:-1 |
3145.8809 |
5 |
0 |
2.6782 |
0 |
| MT-ND2:MT:4733-5189:1 |
955.6573 |
5 |
0 |
1.8467 |
0 |
| TPT1:13:45337122-45340004:-1 |
3195.3669 |
5 |
0 |
3.4640 |
0 |
| RPS27:1:153990762-153992155:1 |
4373.4251 |
5 |
0 |
4.2233 |
0 |
| MT-CYB:MT:15078-15576:1 |
1088.1994 |
5 |
0 |
1.9802 |
0 |
| RPS29:14:49583524-49586533:-1 |
2518.5700 |
5 |
0 |
4.4243 |
0 |
| RPL32:3:12835989-12840215:-1 |
6748.9485 |
5 |
0 |
3.2966 |
0 |
| TMSB10:2:84905667-84906671:1 |
692.9494 |
5 |
0 |
3.9375 |
0 |
de_genes_pseudo_tm <- t(as.data.frame(str_split(rownames(ATres_tm),":")))[,1]
go_res_pseudo_tm <- enrichGO(de_genes_pseudo_tm, OrgDb = "org.Hs.eg.db", keyType = "SYMBOL" , ont = "BP")
barplot(go_res_pseudo_tm , showCategory = 20)

SUBCLUSTER ANALYSIS (Epithelial T Cells (Normal - Tumor - Metastasis))
peak_counts_matrix <- counts(peaks_sce)
colnames(peak_counts_matrix) <- as.character(peaks_sce$CellIdent)
ept_sce_peak_counts <- counts(peaks_sce)[,colnames(peak_counts_matrix) %like% 'EPITHELIAL_Met|EPITHELIAL_Tumor|EPITHELIAL_Normal']
ept_sce_peak_counts <- ept_sce_peak_counts[rowSums(ept_sce_peak_counts) > 7,]
ept_sce_peak <- SingleCellExperiment(assays = list(counts = ept_sce_peak_counts))
ept_sce_peak$cell_labels <- as.character(peaks_sce$CellIdent)[colnames(peak_counts_matrix) %like% 'EPITHELIAL_Met|EPITHELIAL_Tumor|EPITHELIAL_Normal']
clusters <- quickCluster(ept_sce_peak , method = "igraph")
ept_sce_peak <- computeSumFactors(ept_sce_peak , cluster=clusters)
ept_sce_peak$clusters <- clusters
ept_sce_peak <- logNormCounts(ept_sce_peak)
dec <- modelGeneVarByPoisson(ept_sce_peak)
top <- getTopHVGs(dec , n = 4000)
ept_sce_peak <- denoisePCA(ept_sce_peak, subset.row=top, technical=dec)
ept_sce_peak <- runTSNE(ept_sce_peak)
ept_sce_peak <- runUMAP(ept_sce_peak)
plotPCA(ept_sce_peak , colour_by = "cell_labels")

plotUMAP(ept_sce_peak , colour_by = "cell_labels")

plotTSNE(ept_sce_peak , colour_by = "cell_labels")

library(slingshot)
library(RColorBrewer)
ept_sce_peak <- slingshot(ept_sce_peak, reducedDim='PCA' , clusterLabels = as.character(ept_sce_peak$cell_labels) , start.clus = "EPITHELIAL_Normal" , end.clus = "EPITHELIAL_Met")
embedded <- embedCurves(ept_sce_peak, "UMAP")
embedded <- slingCurves(embedded)[[1]]
embedded <- data.frame(embedded$s[embedded$ord,])
plotUMAP(ept_sce_peak, colour_by="slingPseudotime_1") +
geom_path(data=embedded, aes(x=Dim.1, y=Dim.2), linewidth=1)

library(tradeSeq)
# fit negative binomial GAM
sce <- fitGAM(counts = as.matrix(counts(ept_sce_peak)[top,]), sds = as.SlingshotDataSet(ept_sce_peak))
colnames(sce) <- ept_sce_peak$cell_labels
# test for dynamic expression
ATres_ept <- associationTest(sce)
ATres_ept <- ATres_ept[!(is.na(ATres_ept$pvalue)),]
ATres_ept$padj <- p.adjust(ATres_ept$pvalue)
ATres_ept <- ATres_ept[ATres_ept$meanLogFC > 1,]
topgenes <- rownames(ATres_ept[order(ATres_ept$pvalue), ])[1:100]
pst.ord <- order(ept_sce_peak$slingPseudotime_1, na.last = NA)
heatdata <- assays(ept_sce_peak)$counts[topgenes, pst.ord]
heatclus <- ept_sce_peak$cell_labels[pst.ord]
colnames(heatdata) <- ept_sce_peak$cell_labels
heatdata <- heatdata[ , order(colnames(heatdata))]
heatmap(log1p(as.matrix(heatdata)), Colv = NA, Rowv = NA, ColSideColors = brewer.pal(9,"Set1")[heatclus])

kable(ATres_ept[1:50,], digits = 4, "simple" , n = 100)
| PGC:6:41736711-41737000:-1 |
1029.5933 |
5 |
0 |
1.5904 |
0 |
| SCGB3A1:5:180590105-180590774:-1 |
2490.0308 |
5 |
0 |
4.1040 |
0 |
| SFTPC:8:22163887-22164133:1 |
6174.9340 |
5 |
0 |
7.9799 |
0 |
| SFTPB:2:85658562-85662231:-1 |
5669.4410 |
5 |
0 |
2.5528 |
0 |
| SCGB1A1:11:62422196-62423195:1 |
16337.3109 |
5 |
0 |
6.3240 |
0 |
| WFDC2:20:45470423-45481532:1 |
1706.8031 |
5 |
0 |
1.1953 |
0 |
| SFTPA2:10:79555852-79556268:-1 |
979.6923 |
5 |
0 |
5.6949 |
0 |
| SPINK1:5:147824572-147829629:-1 |
695.4665 |
5 |
0 |
1.5376 |
0 |
| NAPSA:19:50358477-50359061:-1 |
1794.6798 |
5 |
0 |
3.0409 |
0 |
| NAPSA:19:50358477-50358777:-1 |
2824.0092 |
5 |
0 |
3.0380 |
0 |
| SFTPA1:10:79614990-79615455:1 |
2479.2673 |
5 |
0 |
6.5906 |
0 |
| S100P:4:6693898-6697170:1 |
3101.9756 |
5 |
0 |
5.3582 |
0 |
| SFTPB:2:85658150-85658822:-1 |
1828.9203 |
5 |
0 |
2.9809 |
0 |
| SPINK4:9:33240166-33248567:1 |
474.3935 |
5 |
0 |
5.8297 |
0 |
| CD74:5:150401637-150401973:-1 |
2657.4888 |
5 |
0 |
1.9319 |
0 |
| S100P:4:6696824-6697170:1 |
2980.8485 |
5 |
0 |
5.2431 |
0 |
| HOPX:4:56647988-56648340:-1 |
1621.3886 |
5 |
0 |
1.7282 |
0 |
| IFI27:14:94114852-94116695:1 |
1225.0653 |
5 |
0 |
2.1889 |
0 |
| IGFBP2:2:216663997-216664436:1 |
2473.7947 |
5 |
0 |
2.2276 |
0 |
| IFI27:14:94115775-94116695:1 |
1409.7879 |
5 |
0 |
2.1720 |
0 |
| LCN2:9:128151666-128153453:1 |
622.9587 |
5 |
0 |
2.3461 |
0 |
| NPC2:14:74479885-74484466:-1 |
3508.9912 |
5 |
0 |
2.5386 |
0 |
| HLA-DRA:6:32443855-32445046:1 |
5027.2332 |
5 |
0 |
2.2103 |
0 |
| SFTPB:2:85657606-85658145:-1 |
1625.5975 |
5 |
0 |
2.8301 |
0 |
| HLA-DRA:6:32444621-32445046:1 |
3081.8746 |
5 |
0 |
2.2134 |
0 |
| RNASE1:14:20801310-20801788:-1 |
1516.6012 |
5 |
0 |
2.5578 |
0 |
| RPS6:9:19376184-19378727:-1 |
4606.4075 |
5 |
0 |
1.0665 |
0 |
| CYB5A:18:74253272-74253668:-1 |
3007.7724 |
5 |
0 |
1.6456 |
0 |
| AKR1C1:10:4972618-4978020:1 |
2103.8101 |
5 |
0 |
2.3831 |
0 |
| AKR1C1:10:4975759-4977982:1 |
682.7853 |
5 |
0 |
2.3953 |
0 |
| PIGR:1:206928522-206928878:-1 |
354.2333 |
5 |
0 |
1.4147 |
0 |
| SFTPA1:10:79614651-79615083:1 |
0.0000 |
5 |
1 |
6.8267 |
1 |
| SERPINF1:17:1777143-1777565:1 |
684.3960 |
5 |
0 |
2.5297 |
0 |
| LY6E:8:143021974-143022457:1 |
1096.5048 |
5 |
0 |
1.1597 |
0 |
| EEF1A1:6:73517465-73518061:-1 |
4285.0858 |
5 |
0 |
1.0481 |
0 |
| KRT7:12:52245547-52248969:1 |
1489.0571 |
5 |
0 |
1.4510 |
0 |
| SFTPD:10:79937695-79938189:-1 |
1843.9981 |
5 |
0 |
4.4872 |
0 |
| KRT7:12:52248177-52248947:1 |
0.0000 |
5 |
1 |
1.4554 |
1 |
| MSLN:16:768340-768865:1 |
1566.4233 |
5 |
0 |
1.8803 |
0 |
| CEACAM6:19:41771435-41772211:1 |
470.5990 |
5 |
0 |
1.9347 |
0 |
| XAGE1A:X:52497275-52500812:1 |
0.0000 |
5 |
1 |
2.5085 |
1 |
| TSPAN8:12:71125085-71125370:-1 |
1591.7143 |
5 |
0 |
2.4725 |
0 |
| HLA-DRB1:6:32578633-32581697:-1 |
6005.2020 |
5 |
0 |
2.4420 |
0 |
| HLA-DRB1:6:32578668-32580959:-1 |
2930.9694 |
5 |
0 |
2.4491 |
0 |
| CTSH:15:78921723-78922198:-1 |
1665.2678 |
5 |
0 |
2.1827 |
0 |
| CD55:1:207331092-207359974:1 |
1963.3329 |
5 |
0 |
1.3544 |
0 |
| HLA-DRB1:6:32578773-32580252:-1 |
3115.5636 |
5 |
0 |
2.4654 |
0 |
| SFTA2:6:30931353-30932036:-1 |
0.0000 |
5 |
1 |
1.6019 |
1 |
| SFTA2:6:30931353-30931790:-1 |
1372.4962 |
5 |
0 |
1.6014 |
0 |
| VMO1:17:4785285-4785639:-1 |
284.0572 |
5 |
0 |
2.6579 |
0 |
de_genes_pseudo_ept <- t(as.data.frame(str_split(rownames(ATres_ept),":")))[,1]
go_res_pseudo_ept <- enrichGO(de_genes_pseudo_ept, OrgDb = "org.Hs.eg.db", keyType = "SYMBOL" , ont = "BP")
barplot(go_res_pseudo_ept , showCategory = 20)

SUBCLUSTER ANALYSIS (Epithelial T Cells (Tumor - Metastasis))
peak_counts_matrix <- counts(peaks_sce)
colnames(peak_counts_matrix) <- as.character(peaks_sce$CellIdent)
ept2_sce_peak_counts <- counts(peaks_sce)[,colnames(peak_counts_matrix) %like% 'EPITHELIAL_Met|EPITHELIAL_Tumor']
ept2_sce_peak_counts <- ept2_sce_peak_counts[rowSums(ept2_sce_peak_counts) > 7,]
ept2_sce_peak <- SingleCellExperiment(assays = list(counts = ept2_sce_peak_counts))
ept2_sce_peak$cell_labels <- as.character(peaks_sce$CellIdent)[colnames(peak_counts_matrix) %like% 'EPITHELIAL_Met|EPITHELIAL_Tumor']
clusters <- quickCluster(ept2_sce_peak , method = "igraph")
ept2_sce_peak <- computeSumFactors(ept2_sce_peak , cluster=clusters)
ept2_sce_peak$clusters <- clusters
ept2_sce_peak <- logNormCounts(ept2_sce_peak)
dec <- modelGeneVarByPoisson(ept2_sce_peak)
top <- getTopHVGs(dec , n = 4000)
ept2_sce_peak <- denoisePCA(ept2_sce_peak, subset.row=top, technical=dec)
ept2_sce_peak <- runTSNE(ept2_sce_peak)
ept2_sce_peak <- runUMAP(ept2_sce_peak)
plotPCA(ept2_sce_peak , colour_by = "cell_labels")

plotUMAP(ept2_sce_peak , colour_by = "cell_labels")

plotTSNE(ept2_sce_peak , colour_by = "cell_labels")

library(slingshot)
library(RColorBrewer)
ept2_sce_peak <- slingshot(ept2_sce_peak, reducedDim='PCA' , clusterLabels = as.character(ept2_sce_peak$cell_labels) , start.clus = "EPITHELIAL_Tumor" , end.clus = "EPITHELIAL_Met")
embedded <- embedCurves(ept2_sce_peak, "UMAP")
embedded <- slingCurves(embedded)[[1]]
embedded <- data.frame(embedded$s[embedded$ord,])
plotUMAP(ept2_sce_peak, colour_by="slingPseudotime_1") +
geom_path(data=embedded, aes(x=Dim.1, y=Dim.2), linewidth=1)

library(tradeSeq)
# fit negative binomial GAM
sce <- fitGAM(counts = as.matrix(counts(ept2_sce_peak)[top,]), sds = as.SlingshotDataSet(ept2_sce_peak))
colnames(sce) <- ept2_sce_peak$cell_labels
# test for dynamic expression
ATres_ept2 <- associationTest(sce)
ATres_ept2 <- ATres_ept2[!(is.na(ATres_ept2$pvalue)),]
ATres_ept2$padj <- p.adjust(ATres_ept2$pvalue)
ATres_ept2 <- ATres_ept2[ATres_ept2$meanLogFC > 1,]
topgenes <- rownames(ATres_ept2[order(ATres_ept2$pvalue), ])[1:100]
pst.ord <- order(ept2_sce_peak$slingPseudotime_1, na.last = NA)
heatdata <- assays(ept2_sce_peak)$counts[topgenes, pst.ord]
heatclus <- ept2_sce_peak$cell_labels[pst.ord]
colnames(heatdata) <- ept2_sce_peak$cell_labels
heatdata <- heatdata[ , order(colnames(heatdata))]
heatmap(log1p(as.matrix(heatdata)), Colv = NA, Rowv = NA, ColSideColors = brewer.pal(9,"Set3")[heatclus] , scale = "none")

kable(ATres_ept2[1:50,], digits = 4, "simple" , n = 100)
| PGC:6:41736711-41737766:-1 |
1277.5491 |
5 |
0 |
2.2027 |
0 |
| PGC:6:41736711-41737000:-1 |
1277.8313 |
5 |
0 |
2.1955 |
0 |
| SCGB3A1:5:180590105-180590774:-1 |
1586.5862 |
5 |
0 |
1.9502 |
0 |
| SFTPB:2:85658805-85659312:-1 |
1975.7574 |
5 |
0 |
1.9079 |
0 |
| SPINK1:5:147824572-147831671:-1 |
721.9681 |
5 |
0 |
3.5798 |
0 |
| SPINK4:9:33240166-33248567:1 |
945.3896 |
5 |
0 |
3.6358 |
0 |
| S100P:4:6693898-6697170:1 |
3200.5643 |
5 |
0 |
2.2581 |
0 |
| NAPSA:19:50358477-50359061:-1 |
1465.7034 |
5 |
0 |
2.0227 |
0 |
| S100P:4:6696824-6697170:1 |
3016.9293 |
5 |
0 |
2.2227 |
0 |
| SFTPB:2:85660165-85660981:-1 |
1326.8796 |
5 |
0 |
2.6839 |
0 |
| SFTPB:2:85660315-85660717:-1 |
1333.7550 |
5 |
0 |
2.6380 |
0 |
| SLPI:20:45252239-45253580:-1 |
768.9916 |
5 |
0 |
2.6550 |
0 |
| FTL:19:48966284-48966879:1 |
2193.1718 |
5 |
0 |
1.3198 |
0 |
| SFTPB:2:85658150-85658822:-1 |
1410.8368 |
5 |
0 |
1.8659 |
0 |
| CD74:5:150401637-150401973:-1 |
1818.1578 |
5 |
0 |
2.1150 |
0 |
| SOD3:4:24800362-24800842:1 |
1445.2993 |
5 |
0 |
2.6268 |
0 |
| AGR2:7:16792612-16793014:-1 |
1147.8904 |
5 |
0 |
3.3719 |
0 |
| IFI27:14:94114852-94116695:1 |
858.0862 |
5 |
0 |
2.6317 |
0 |
| PPDPF:20:63521481-63522206:1 |
3467.5106 |
5 |
0 |
2.0845 |
0 |
| TFF3:21:42312039-42315373:-1 |
198.8671 |
5 |
0 |
3.5735 |
0 |
| TFF3:21:42312033-42313694:-1 |
208.0968 |
5 |
0 |
3.5863 |
0 |
| SERPINF1:17:1777143-1777565:1 |
1206.0608 |
5 |
0 |
2.7086 |
0 |
| RPL39:X:119786504-119790000:-1 |
2740.1048 |
5 |
0 |
2.4129 |
0 |
| S100A6:1:153534599-153535280:-1 |
112.7421 |
5 |
0 |
2.9294 |
0 |
| TMSB4X:X:12976812-12977227:1 |
1778.6640 |
5 |
0 |
2.4642 |
0 |
| SFTPB:2:85657606-85658145:-1 |
1379.0654 |
5 |
0 |
1.7847 |
0 |
| ATP5F1E:20:59028521-59032345:-1 |
3991.9835 |
5 |
0 |
1.6707 |
0 |
| HLA-B:6:31353872-31354199:-1 |
1870.4677 |
5 |
0 |
2.4973 |
0 |
| CLU:8:27597946-27599793:-1 |
561.5024 |
5 |
0 |
1.4735 |
0 |
| CLU:8:27597929-27598644:-1 |
415.6819 |
5 |
0 |
1.4756 |
0 |
| DHFR:5:80651395-80651616:-1 |
918.2876 |
5 |
0 |
1.2883 |
0 |
| HLA-DRA:6:32443855-32445046:1 |
2034.5723 |
5 |
0 |
1.0603 |
0 |
| B2M:15:44717694-44718214:1 |
3001.6915 |
5 |
0 |
2.8707 |
0 |
| HLA-DRA:6:32444621-32445046:1 |
1985.7111 |
5 |
0 |
1.0573 |
0 |
| RPS6:9:19376184-19378727:-1 |
3488.1565 |
5 |
0 |
1.6802 |
0 |
| RPL41:12:56116625-56117848:1 |
16726.7135 |
5 |
0 |
2.1018 |
0 |
| RPL10:X:154399881-154400938:1 |
4242.5284 |
5 |
0 |
1.6979 |
0 |
| XAGE1A:X:52495834-52500807:1 |
1282.7822 |
5 |
0 |
1.8259 |
0 |
| NPC2:14:74479885-74484466:-1 |
1895.6520 |
5 |
0 |
1.6551 |
0 |
| CYB5A:18:74253272-74253668:-1 |
1811.5637 |
5 |
0 |
1.2801 |
0 |
| NPC2:14:74479896-74480794:-1 |
1891.2206 |
5 |
0 |
1.6427 |
0 |
| RPS2:16:1962058-1962611:-1 |
2548.6678 |
5 |
0 |
1.8344 |
0 |
| RPL23A:17:28720787-28723972:1 |
2829.7605 |
5 |
0 |
2.4118 |
0 |
| MT-CO1:MT:5904-7108:1 |
1094.8221 |
5 |
0 |
1.0076 |
0 |
| TSPAN8:12:71125085-71125370:-1 |
1752.7212 |
5 |
0 |
3.0847 |
0 |
| RPS27:1:153990762-153992155:1 |
5961.1191 |
5 |
0 |
2.9179 |
0 |
| CEACAM6:19:41771435-41772211:1 |
277.5793 |
5 |
0 |
1.7011 |
0 |
| TPT1:13:45337122-45340004:-1 |
3750.8162 |
5 |
0 |
2.7154 |
0 |
| SCGB1A1:11:62419063-62423195:1 |
502.1654 |
5 |
0 |
3.5902 |
0 |
| VMO1:17:4785285-4785639:-1 |
809.3609 |
5 |
0 |
1.4955 |
0 |
de_genes_pseudo_ept2 <- t(as.data.frame(str_split(rownames(ATres_ept2),":")))[,1]
go_res_pseudo_ept2 <- enrichGO(de_genes_pseudo_ept2, OrgDb = "org.Hs.eg.db", keyType = "SYMBOL" , ont = "BP")
barplot(go_res_pseudo_ept2 , showCategory = 20)
